1
|
Alexandria SJ, Hudgens MG, Aiello AE. Assessing intervention effects in a randomized trial within a social network. Biometrics 2023; 79:1409-1419. [PMID: 34825368 PMCID: PMC9133268 DOI: 10.1111/biom.13606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 11/15/2021] [Accepted: 11/18/2021] [Indexed: 11/29/2022]
Abstract
Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.
Collapse
Affiliation(s)
- Shaina J. Alexandria
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, U.S.A
| | - Michael G. Hudgens
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
| | - Allison E. Aiello
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A
| |
Collapse
|
2
|
Bofill Roig M, Krotka P, Burman CF, Glimm E, Gold SM, Hees K, Jacko P, Koenig F, Magirr D, Mesenbrink P, Viele K, Posch M. On model-based time trend adjustments in platform trials with non-concurrent controls. BMC Med Res Methodol 2022; 22:228. [PMID: 35971069 PMCID: PMC9380382 DOI: 10.1186/s12874-022-01683-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial's efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. METHODS We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. RESULTS A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. CONCLUSIONS The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.
Collapse
Affiliation(s)
- Marta Bofill Roig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Pavla Krotka
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Carl-Fredrik Burman
- Statistical Innovation, Data Science & Artificial Intelligence, AstraZeneca, Gothenburg, Sweden
| | - Ekkehard Glimm
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
- Institute of Biometry and Medical Informatics, University of Magdeburg, Magdeburg, Germany
| | - Stefan M Gold
- Klinik für Psychiatrie und Psychotherapie, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Medizinische Klinik m.S. Psychosomatik, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institut für Neuroimmunologie und Multiple Sklerose (INIMS), Zentrum für Molekulare Neurobiologie, Universitätsklinikum Hamburg Eppendorf, Hamburg, Germany
| | - Katharina Hees
- Section of Biostatistics, Paul-Ehrlich-Institut, Langen, Germany
| | - Peter Jacko
- Berry Consultants, Abingdon, UK
- Lancaster University, Lancaster, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Dominic Magirr
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Peter Mesenbrink
- Analytics Global Drug Development, Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | | | - Martin Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
| |
Collapse
|
3
|
Zhou Y, Turner EL, Simmons RA, Li F. Constrained randomization and statistical inference for multi‐arm parallel cluster randomized controlled trials. Stat Med 2022; 41:1862-1883. [PMID: 35146788 PMCID: PMC9007899 DOI: 10.1002/sim.9333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 12/17/2022]
Abstract
A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi-arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi-arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs in multi-arm cRCTs, with varying combinations of design and analysis-based covariate adjustment strategies. In particular, as randomization-based tests have not been extensively studied in multi-arm cRCTs, we additionally develop most-powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model-based and randomization-based analyses could gain power while preserving nominal type I error rate, given proper analysis-based adjustment for the baseline covariates. Randomization-based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi-arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space.
Collapse
Affiliation(s)
- Yunji Zhou
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Elizabeth L. Turner
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Ryan A. Simmons
- Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA
- Duke Global Health Institute Duke University Durham North Carolina USA
| | - Fan Li
- Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
- Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven Connecticut USA
| |
Collapse
|
4
|
Ryeznik Y, Sverdlov O, Svensson EM, Montepiedra G, Hooker AC, Wong WK. Pharmacometrics meets statistics-A synergy for modern drug development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1134-1149. [PMID: 34318621 PMCID: PMC8520751 DOI: 10.1002/psp4.12696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 01/20/2023]
Abstract
Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.
Collapse
Affiliation(s)
- Yevgen Ryeznik
- BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, R&D Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Oleksandr Sverdlov
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Elin M Svensson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.,Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Grace Montepiedra
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, Los Angeles, California, USA
| |
Collapse
|
5
|
A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials. Stat Pap (Berl) 2021. [DOI: 10.1007/s00362-021-01234-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.
Collapse
|
6
|
Wang Y, Rosenberger WF. Randomization-based interval estimation in randomized clinical trials. Stat Med 2020; 39:2843-2854. [PMID: 32491198 DOI: 10.1002/sim.8577] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 11/08/2022]
Abstract
Randomization-based interval estimation takes into account the particular randomization procedure in the analysis and preserves the confidence level even in the presence of heterogeneity. It is distinguished from population-based confidence intervals with respect to three aspects: definition, computation, and interpretation. The article contributes to the discussion of how to construct a confidence interval for a treatment difference from randomization tests when analyzing data from randomized clinical trials. The discussion covers (i) the definition of a confidence interval for a treatment difference in randomization-based inference, (ii) computational algorithms for efficiently approximating the endpoints of an interval, and (iii) evaluation of statistical properties (ie, coverage probability and interval length) of randomization-based and population-based confidence intervals under a selected set of randomization procedures when assuming heterogeneity in patient outcomes. The method is illustrated with a case study.
Collapse
Affiliation(s)
- Yanying Wang
- Department of Statistics, George Mason University, Fairfax, Virginia, USA
| | | |
Collapse
|